The community has always been interested in development of more sophisticated algorithms and applying them to real-life tasks. RL is not far-behind in this. With the practical viability of deep leaning, there have been significant progresses in training RL algorithms with deep learning and then applying them to solve problems with human-level accuracy. This has been lately demonstrated by the use of RL algorithms to train agents for playing atari games, where they have surpassed human accuracy on a wide array of games, without significant changes in the strategy [1]}. Board games are also not left behind in this feat. Shortly after atari games, RL has been used to solve one of the most complex board games, and beat the world champion [2]}.
